Parameter significance

As shovra previously (fig.l), the con )lex influence of the metal ions even on viscoelastic properties of the gels might be sufficiently demonstrated by considering the border lines of each defined field only (fig.2-4). The squares of the adjusted correlation coefficirats lay between 0.9778 and 0.9965 for G and between 0.9951 and 0.9996 for G /G . The resulting sinple models only contained parameters significant at levels with a < 0.15, describing direct influences of each of the three cations in one set and interactions between two cations. 

FIGURE 1. Effect of acute METH on serotonergic parameters Significantly different from control, p<0.05 by Student s t-test. 

Table 6-6 Parameters Significantly Affecting the Behavior of Explosions...

Luster et al., 1992a, b). Specifically, the use of either a humoral response assay for plaque-forming colonies (PFC response) or determination of surface marker expression in combination with almost any other parameter significantly increased the ability to predict immunotoxicity when compared to the predictivity of any assay alone. 

Despite the obvious correspondence between scaled elasticities and saturation parameters, significant differences arise in the interpretation of these quantities. Within MCA, the elasticities are derived from specific rate functions and measure the local sensitivity with respect to substrate concentrations [96], Within the approach considered here, the saturation parameters, hence the scaled elasticities, are bona fide parameters of the system without recourse to any specific functional form of the rate equations. Likewise, SKM makes no distinction between scaled elasticities and the kinetic exponents within the power-law formalism. In fact, the power-law formalism can be regarded as the simplest possible way to specify a set of explicit nonlinear functions that is consistent with a given Jacobian. Nonetheless, SKM seeks to provide an evaluation of parametric representation directly, without going the loop way via auxiliary ad hoc functions. 

Fig. 8 Only the viscosities Vj and V3 can influence the critical parameters significantly. The upper row depicts the dependence on a isotropic variation of the viscosity. In the middle and lower row we present the variation with v2 and V3 setting the other viscosities to v, = 0.1. Here the thick solid lines represent the minimal set of variables. For the full set of variables we have chosen four different values of X the solid curves with X = 0.7, the dashed curves with X = 1.3, the dotted curves with X = 2 and the dot-dashed curves with X = 3.5. Note the similarities between the curves for small (solid) and large X (dot-dashed) in the upper and middle row. In these regimes v2 is the dominating viscosity...

There are many examples of CNS diseases where a combination of more than one CSF parameter significantly improves the accuracy of the diagnosis. Neuro-inflammatory diseases, like neuroborreliosis (Tumani et al., 1995) or multiple sclerosis (Reiber et al., 1998) are representative examples. Similarly, studies have been reported showing increased sensitivity and specificity of a combination of CSF parameters in early and differential diagnoses of AD. 

Conclusions Film Conversion Parameter Significance of the Hatta Number... 

Most of the correlations discussed in sections ll.B and ll.C estimate the mechanical properties of polymers in terms of several material parameters of a more fundamental nature. Creep, stress relaxation and fatigue, which are all very important in determining durability, are discussed in Section 1 l.D. As discussed in Section 1 l.E, the key contribution of our work is the development of the general correlations presented in this book to calculate the fundamental material parameters, significantly extending the range and structural diversity of polymers for which existing structure-property relationships for the mechanical properties can be applied. 

Several solvent properties could be included in an LFER for medium effects (31). However, the general approach, as shown previously, has been to restrict treatment to two parameters, either because of the difficulty in assessing the significance of the additional parameters (31) or, as in the Swain work (36), because it is believed that only two parameters are required. Certainly, additional parameters will give better fit to experimental data, but can this improved fit be attributed to a physical dependence on the new parameter or simply to an additional degree of adjustment In a series of recent papers, Taft et al. (37) have adopted the approach of including as parameters in an LFER all those solvent characteristics for which evidence of involvement in medium effects exists they reasoned that modern computational methods will permit statistical evaluation of parameter significance. At present, the equation in use for kinetic phenomena has four parameters ... 

We recently discovered a novel polymorph, designated form 6, with unit cell parameters significantly different from both forms [29], Single crystal X-ray structures of form 3 (melt phase), form 4 (hydrate/solvate) and form 5 (sublimed phase) [30] are not known and they are therefore not relevant to the present discussion on molecular conformations and multiple Z. The conformation of molecule 10 in form 1 and 2 is different only in the opposite orientation of OCH3 group, while the rest of the tricyclic skeleton overlays very nicely. Interestingly, form 6 has both these... 

Despite the indications given above, it will often be impossible to reduce the number of standard parameters significantly, or to visualize suitable linear combinations of parameters that may help in this aim. We are then left with a truly multivariate problem. However, one of the aims of multivariate analysis is the reduction of dimensionality, i.e. the detection of a subset of parameters or, more often, of linear combinations thereof, that best describe the total variance in the data set. In effect, the techniques are trying to detect automatically the set of axes in parameter space that are most useful for visualizing the data. We return to this topic in some depth in Section 4.6.3. 

On a practical level, the heuristic approach includes first collecting all the possible data during the experiments as a function of the parameters which are deemed to be important, i.e. concentrations, temperature, pressures, pH, catalyst concentration, volume, etc. Then the rate constants are estimated by regression analysis and the adequacy of the model is judged based on some criteria (like residual sums and parameter significance, which will be discussed further). If a researcher is not satisfied, then additional experiments are performed, followed by parameter estimation and sometimes simulations outside the studied parameter domain. The latter procedure provides the possibility to test the predictive power of a kinetic model. The kinetic model is then gradually improved and the experimental plan is modified, if needed. This process continues until the researcher is satisfied with the kinetic model. 

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